def test_filters():
"""Test low-, band-, high-pass, and band-stop filters plus resampling."""
sfreq = 100
sig_len_secs = 15
a = rng.randn(2, sig_len_secs * sfreq)
# let's test our catchers
for fl in ['blah', [0, 1], 1000.5, '10ss', '10']:
pytest.raises(ValueError,
filter_data,
a,
sfreq,
4,
8,
None,
fl,
1.0,
1.0,
fir_design='firwin')
for nj in ['blah', 0.5]:
pytest.raises(ValueError,
filter_data,
a,
sfreq,
4,
8,
None,
1000,
1.0,
1.0,
n_jobs=nj,
phase='zero',
fir_design='firwin')
pytest.raises(ValueError,
filter_data,
a,
sfreq,
4,
8,
None,
100,
1.,
1.,
fir_window='foo')
pytest.raises(ValueError,
filter_data,
a,
sfreq,
4,
8,
None,
10,
1.,
1.,
fir_design='firwin') # too short
# > Nyq/2
pytest.raises(ValueError,
filter_data,
a,
sfreq,
4,
sfreq / 2.,
None,
100,
1.0,
1.0,
fir_design='firwin')
pytest.raises(ValueError,
filter_data,
a,
sfreq,
-1,
None,
None,
100,
1.0,
1.0,
fir_design='firwin')
# these should work
create_filter(None, sfreq, None, None)
create_filter(a, sfreq, None, None, fir_design='firwin')
create_filter(a, sfreq, None, None, method='iir')
# check our short-filter warning:
with pytest.warns(RuntimeWarning, match='attenuation'):
# Warning for low attenuation
filter_data(a, sfreq, 1, 8, filter_length=256, fir_design='firwin2')
with pytest.warns(RuntimeWarning, match='Increase filter_length'):
# Warning for too short a filter
filter_data(a, sfreq, 1, 8, filter_length='0.5s', fir_design='firwin2')
# try new default and old default
freqs = fftfreq(a.shape[-1], 1. / sfreq)
A = np.abs(fft(a))
kwargs = dict(fir_design='firwin')
for fl in ['auto', '10s', '5000ms', 1024, 1023]:
bp = filter_data(a, sfreq, 4, 8, None, fl, 1.0, 1.0, **kwargs)
bs = filter_data(a, sfreq, 8 + 1.0, 4 - 1.0, None, fl, 1.0, 1.0,
**kwargs)
lp = filter_data(a,
sfreq,
None,
8,
None,
fl,
10,
1.0,
n_jobs=2,
**kwargs)
hp = filter_data(lp, sfreq, 4, None, None, fl, 1.0, 10, **kwargs)
assert_allclose(hp, bp, rtol=1e-3, atol=1e-3)
assert_allclose(bp + bs, a, rtol=1e-3, atol=1e-3)
# Sanity check ttenuation
mask = (freqs > 5.5) & (freqs < 6.5)
assert_allclose(np.mean(np.abs(fft(bp)[:, mask]) / A[:, mask]),
1.,
atol=0.02)
assert_allclose(np.mean(np.abs(fft(bs)[:, mask]) / A[:, mask]),
0.,
atol=0.2)
# now the minimum-phase versions
bp = filter_data(a,
sfreq,
4,
8,
None,
fl,
1.0,
1.0,
phase='minimum',
**kwargs)
bs = filter_data(a,
sfreq,
8 + 1.0,
4 - 1.0,
None,
fl,
1.0,
1.0,
phase='minimum',
**kwargs)
assert_allclose(np.mean(np.abs(fft(bp)[:, mask]) / A[:, mask]),
1.,
atol=0.11)
assert_allclose(np.mean(np.abs(fft(bs)[:, mask]) / A[:, mask]),
0.,
atol=0.3)
# and since these are low-passed, downsampling/upsampling should be close
n_resamp_ignore = 10
bp_up_dn = resample(resample(bp, 2, 1, n_jobs=2), 1, 2, n_jobs=2)
assert_array_almost_equal(bp[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# note that on systems without CUDA, this line serves as a test for a
# graceful fallback to n_jobs=1
bp_up_dn = resample(resample(bp, 2, 1, n_jobs='cuda'), 1, 2, n_jobs='cuda')
assert_array_almost_equal(bp[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# test to make sure our resamling matches scipy's
bp_up_dn = sp_resample(sp_resample(bp,
2 * bp.shape[-1],
axis=-1,
window='boxcar'),
bp.shape[-1],
window='boxcar',
axis=-1)
assert_array_almost_equal(bp[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# make sure we don't alias
t = np.array(list(range(sfreq * sig_len_secs))) / float(sfreq)
# make sinusoid close to the Nyquist frequency
sig = np.sin(2 * np.pi * sfreq / 2.2 * t)
# signal should disappear with 2x downsampling
sig_gone = resample(sig, 1, 2)[n_resamp_ignore:-n_resamp_ignore]
assert_array_almost_equal(np.zeros_like(sig_gone), sig_gone, 2)
# let's construct some filters
iir_params = dict(ftype='cheby1', gpass=1, gstop=20, output='ba')
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter
assert iir_params['a'].size - 1 == 3
assert iir_params['b'].size - 1 == 3
iir_params = dict(ftype='butter', order=4, output='ba')
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert iir_params['a'].size - 1 == 4
assert iir_params['b'].size - 1 == 4
iir_params = dict(ftype='cheby1', gpass=1, gstop=20)
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter, which requires 2 SOS ((2, 6))
assert iir_params['sos'].shape == (2, 6)
iir_params = dict(ftype='butter', order=4, output='sos')
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert iir_params['sos'].shape == (2, 6)
# check that picks work for 3d array with one channel and picks=[0]
a = rng.randn(5 * sfreq, 5 * sfreq)
b = a[:, None, :]
a_filt = filter_data(a,
sfreq,
4,
8,
None,
400,
2.0,
2.0,
fir_design='firwin')
b_filt = filter_data(b,
sfreq,
4,
8, [0],
400,
2.0,
2.0,
fir_design='firwin')
assert_array_equal(a_filt[:, None, :], b_filt)
# check for n-dimensional case
a = rng.randn(2, 2, 2, 2)
with pytest.warns(RuntimeWarning, match='longer'):
pytest.raises(ValueError, filter_data, a, sfreq, 4, 8,
np.array([0, 1]), 100, 1.0, 1.0)
# check corner case (#4693)
h = create_filter(np.empty(10000),
1000.,
l_freq=None,
h_freq=55.,
h_trans_bandwidth=0.5,
method='fir',
phase='zero-double',
fir_design='firwin',
verbose=True)
assert len(h) == 6601
def _my_trans(data):
"""FFT that adds an additional dimension by repeating result."""
data_t = fft(data)
data_t = np.concatenate([data_t[:, :, None], data_t[:, :, None]], axis=2)
return data_t, None